Abstract 449: Machine learning models identify histological features that can predict KEAP1 mutations in lung adenocarcinoma
Authors: Robert Egger, Martin Ieong, Murray Resnick, Amaro Taylor-Weiner, Victoria Mountain, Ilan Wapinski, Michael Montalto, Andrew Beck, Josie Hayes, Benjamin Glass
Journal: Cancer Research (2022)

Abstract Background: In lung cancer, the KEAP1/NRF2 pathway modulates an anti-tumor response by regulating cellular metabolism and inflammatory processes. Approximately 19% of lung adenocarcinoma (LUAD) tumors have a mutation in KEAP1. Clinically, KEAP1-mutated LUAD has poor prognosis, and there is need for rapid and accurate patient genotyping to inform treatment decisions. Here, we describe machine learning (ML) models that can predict KEAP1 mutation status from tissue histology. Methods: ML models, pre-trained to identify and quantify areas of tissue (cancer epithelium, cancer stroma, and necrosis), counts of cancer, fibroblast, and immune cells (lymphocytes, macrophages, plasma cells) in non-small cell lung cancer (NSCLC), were applied to 208 hematoxylin and eosin (H&amp;E)-stained whole slide images (WSI) of LUAD from The Cancer Genome Atlas (TCGA) without further training. Genomic analyses indicated that 17% (N=35) of these cases are KEAP1MUT. Human Interpretable Features (HIFs), based on histology predictions, are automatically extracted from the model and provide a quantitative description of the tumor microenvironment of each WSI. Associations between HIFs and KEAP1MUT were determined by univariate analysis followed by false discovery rate (FDR) correction. Hierarchical clustering using cross correlation and combining p-values for HIF groups using an Empirical Brown’s method identified correlations between HIFs. Confounding factor influence was accounted for after positive associations were identified. Independent validation of associations between KEAP1MUT and HIFs was performed using TCGA transcriptomic data to correlate specific mutations with mRNA expression of relevant markers. Results: ML-models generated 4,443 HIFs from the TCGA LUAD WSI, which were reduced to 2,684 HIFs after removal of outlier HIFs, exclusion of HIFs that are degenerate, have missing features, or are of an absolute value. KEAP1MUT was significantly associated with 193 HIFs in univariate analyses (p&amp;lt;0.05 after FDR correction), and four groups of HIFs after taking correlations into account (p&amp;lt;0.05 after group-wise FDR correction; 211-264 HIFs per group). 161 HIFs were identified by both methods. Further assessment of the KEAP1MUT -associated HIFs showed that these mutations were correlated with a reduction in macrophages in the tumor microenvironment (TME). This was supported by analysis of transcriptomic data from KEAP1MUT (N=35) and KEAP1WT (N=164) samples, which showed significantly reduced expression of the macrophage marker CD14 (p&amp;lt;0.001) in KEAP1MUT tissue samples. Conclusions: ML model quantification of TME histological features can generate HIFs that correlate with the KEAP1MUT status of a LUAD biopsy. These results exemplify how ML-powered digital pathology could predict molecular markers directly from standard H&amp;E biopsy slides. Citation Format: Robert Egger, Martin Ieong, Murray Resnick, Amaro Taylor-Weiner, Victoria Mountain, Ilan Wapinski, Michael Montalto, Andrew Beck, Josie Hayes, Benjamin Glass. Machine learning models identify histological features that can predict KEAP1 mutations in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 449.